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The Senior Principal Cloud Data Engineer is a seniorlevel individual contributor responsible for defining, building, and evolving enterprise cloud data platform capabilities in Microsoft Azure. This role provides deep technical leadership across cloud data architecture, platform engineering, and enablement, ensuring that enterprise data solutions are scalable, secure, and consistent with M&T's data and technology strategies.

The role partners closely with Enterprise Architecture and the Data organization to help define and drive the enterprise data strategy from a cloud platform perspective. In addition to influencing strategy, this role is accountable for owning and engineering the associated Azurebased data platform capabilities that enable analytics, reporting, and datadriven solutions across the enterprise.

The position operates primarily upstream of data solution delivery, focusing on platform standards, reusable capabilities, and architectural alignment while remaining grounded in handson engineering and realworld implementation.

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Enterprise Cloud Data Platform Leadership

• Define and influence the architecture and engineering standards for enterprise cloud data platforms in Azure.

• Serve as a senior technical authority for cloud data platform decisions, including tradeoffs across scalability, security, performance, and cost.

• Provide technical leadership for the evolution of shared data capabilities used by analytics, reporting, and application teams.

Data Strategy Partnership

• Partner closely with Enterprise Architecture and the Data organization to: Help shape and refine the enterprise data strategy, Translate data strategy into actionable cloud platform capabilities, Ensure cloud data platform designs align with enterprise data models, governance standards, and longterm architectural direction.

• Act as a technical bridge between data strategy and engineering execution.

Cloud Data Platform Engineering (Azure)

• Own the design and buildout of core Azurebased data platform capabilities, including but not limited to: Data ingestion and integration patterns, Enterprise data storage and lakehouse architectures, Data processing and transformation platforms, Analytics and consumption enablement patterns

• Establish opinionated, reusable platform components and reference implementations rather than oneoff data solutions.

• Ensure data platforms are built with automation, resilience, and security by design.

Standards, Patterns & Reference Architectures

• Develop and maintain approved reference architectures and technical patterns for cloudbased data solutions.

• Define standards for: Data pipelines and integration approaches, Storage, partitioning, and lifecycle management, Access control, encryption, and data protection, Monitoring, observability, and operational support

• Ensure patterns are adoptionready and consumable by delivery teams.

Engineering Enablement

• Reduce delivery friction for data engineers and analytics teams by providing: Clear technical guidance, Reusable templates and frameworks, Platformembedded controls and defaults

• Promote consistency and reuse across data solutions while enabling appropriate flexibility for usecasedriven needs.

• Influence adoption through engineering leadership rather than mandate.

HandsOn Technical Contribution

• Remain actively engaged in: Architecture and design reviews, Proofs of concept and technical validations, Platform pilots and earlystage data initiatives

• Validate that cloud data standards and platforms are practical, scalable, and operationally viable.

• Provide technical mentorship to senior cloud and data engineers.

CrossFunctional Collaboration

• Work in close partnership with: Enterprise and solution architecture, Data engineering, analytics, and governance teams, Cloud and infrastructure engineering, Security and risk management teams

• Communicate data platform decisions and standards clearly to engineering leadership and senior technology stakeholders.

• Ensure alignment between cloud platform capabilities and enterprise data objectives.

• Senior individual contributor role with enterpriselevel impact.

• Direct accountability for the design and buildout of shared cloud data platform capabilities in Azure.

• No direct peoplemanagement or budget ownership.

• Success measured by:

  • Adoption of enterprise data platform capabilities

  • Reduction in bespoke or inconsistent data solutions

  • Improved scalability and reliability of data workloads

  • Alignment between data strategy and platform execution

Combined minimum of 8 years' higher education and/or work experience in systems design, management and/or architecture

Strong understanding of the system development and infrastructure lifecycle and architecture, vendor best practices, IT Service Management, and systems design

• Bachelor's degree in Computer Science, Engineering, or equivalent practical experience

• Significant experience in data engineering or platform engineering roles

• Demonstrated expertise in:

  • Azure data services and architectures

  • Cloudbased data integration and processing

  • Platformengineering approaches for data solutions

  • Infrastructure as Code and automation

• Experience operating in largescale or regulated enterprise environments

• Background in enterprise data platforms, analytics enablement, or data modernization

• Familiarity with data governance, data quality, and operational data management practices

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